ORGANIZATIONAL SEMIOTICS: Evolving a Science of Information Systems

IFIP • The International Federation for Information Processing

IFIP was founded in 1960 under the auspices of UNESCO, following the First World Computer Congress held in Paris the previous year. An umbrella organization for societies working in information processing, IFIP's aim is two-fold: to support information processing within its member countries and to encourage technology transfer to developing nations. As its mission statement clearly states,

IFIP's mission is to be the leading, truly international, apolitical organization which encourages and assists in the development, exploitation and application of information technology for the benefit of all people.

IFIP is a non-profitmaking organization, run almost solely by 2500 volunteers. It operates through a number of technical committees, which organize events and publications. IFIP's events range from an international congress to local seminars, but the most important are:

• The IFIP World Computer Congress. held every second year; • open conferences; • working conferences.

The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high.

As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed.

The working conferences are structured differently. They are usually run by a working group and attendance is small and by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is less rigorous and papers are subjected to extensive group discussion.

Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers.

Any national society whose primary activity is in information may apply to become a full member ofIFIP, although full membership is restricted to one society per country. Full members are entitled to vote at the annual General Assembly, National societies preferring a less committed involvement may apply for associate or corresponding membership. Associate members enjoy the same benefits as full members, but without voting rights. Corresponding members are not represented in IFIP bodies. Affiliated membership is open to non-national societies, and individual and honorary membership schemes are also offered.

ORGANIZATIONAL SEMIOTICS Evolving a Science of Information Systems

IFIP TCB / WGB. 1 Working Conference on Organizational Semiotics: Evolving a Science of Information Systems July 23-25, 2001, Montreal, Quebec, Canada

Edited by

Kecheng liu The University of Reading United Kingdom

Rodney J. Clarke University of Wollongong Australia

Peter 80gh Andersen University of Aalborg Denmark

Ronald K. Stamper University of T wente / Staffordshire University The Netherlands / United Kingdom

with

EI-Sayed Abou-Zeid Concordia University Canada

SPRINGER SCIENCE+BUSINESS MEDIA, LLC

Library of Congress Cataloging-in-Publication Data

IFIP TC81WG8.1 Working Conference on Organizational Semiotics: Evolving a Science of Information Systems (2001 : Montreal, Quebec)

Organizational semiotics: evolving a science of information systems : IFIP TC81WG8.1 Working Conference on Organizational Semiotics, Evolving a Science oflnformation Systems / edited by Kecheng Liu ... [et al.].

Includes bibliographical references and index. ISBN 978-1-4757-6111-5 ISBN 978-0-387-35611-2 (eBook) DOI 10.1007/978-0-387-35611-2 1. Management information systems-Congresses.

effectiveness-Congresses. 3. Semiotics-Congresses. management-Congresses. 1. Liu, Kecheng, 1957-. II. Title.

2.0rganizational 4. Knowledge

94. IV. Studies in organisational semiotics (Series) ; 3.

T58.6 1362 2001 658.4'038-dc21

Copyright © 2002 by Springer Science+Business Media New York Originally published by Kluwer Academic Publishers in 2002

2002028514

AH rights reserved. No part of this work may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photo­copying, microfilming, recording, or otherwise, without written permission from the Publisher Springer Science+Business Media, LLC. with the exception of any material supplied specifically for the purpose of being entered and executed on a computer system, for exclusive use by the purchaser of the work.

Printed on acid-free paper.

by IFIP International Federation for Information Processing

The original version of the book frontmatter was revised: The copyright line was incorrect. The Erratum to the book frontmatter is available at DOI: 10.1007/978-0-387-35611-2_22

Contents

Contributors Vll

Preface Xlll

Exploring the Explanatory Power of Actability 1 PAR J. AGERFALK, FREDRIK KARLSSON, ANDERS IDALMARSSON

Information and Knowledge Economies 21 DANIEL O'CONNOR, ROB SHIELDS, SUZAN !LCAN, AND EDWINA TABORSKY

Data. Information. and Knowledge 41 FRIEDER NAKE

Organisations as Practice Systems 51 GORAN GOLDKUHL, ANNIE ROSTLINGER, EWA BRAF

Knowledge or Information 71 EWABRAF

Translation, Betrayal and Ambiguity in IS Development 91 JIM UNDERWOOD

Integrating Information Systems 109 VALERIE A. MARTIN, MARK L YCETI AND ROBERT MACREDIE

User-System Interface Design 119 JOHN H. CONNOLLY AND LAIN W. PHILLIPS

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Contributors

Par J. Agerfalk Dept. of Infonnatics (ESI), Orebro University, SE 701 82 Orebro, Sweden, Phone: +46 19 303000

Peter B0gb Andersen Department of Computer Science/Center for Human Machine Interaction, University of Aalborg

M. Cecilia C. Baranauskas Institute of Computing- IC, State University of Campinas - Unicamp, Cidade Universitana Zeferino Vaz, Barao Geraldo, Campinas - SP - Brazil, 13083-970, phone: +55 19788-5870, fax: +55 19788-7470, cecilia@ic.unicamp.br

Roberto S. Bigonba Computer Science Department-lCEX, Federal University of Minas Gerais, Av. Antonio Carlos 6627, Belo Horizonte - MG - Brazil 31270-010, phone: +5531499-5860, fax: +5531499-5858, {jusalles, bigonha}@dcc.ufmg.br

EwaBraf Jonkoping International Business School, P.O. 1026, S-551 11 Jonkoping, Sweden, phone: +4636156178, fax: +4636 121832 and Centre for studies on Humans, Technology and Organisation (CMTO), VITS Research Group, Linkoping University, Sweden, ewa.braf@ihh.hj.se

Vlll

Rodney J. Clarke Department of Information Systems, University of Wollongong, Northfields Avenue, North Wollongong, NSW AUSTRALIA, Tel: +61 2 4221 3752 Fax: +61 242221 4474 Email: rodney_clarke@uow.edu.au

John H. Connolly Department of Computer Science, Loughborough University, Loughborough, LEll 3TU, UK. Telephone: +44 1509 222943. Fax: +44 1509211586. Email: J.H.Connolly@lboro.ac.uk

AldodeMoor Infolab, Tilburg University, P.O.Box 90153, 5000 LE Tilburg, The Netherlands, tel. +31-13-4663020, fax +31-13-4663069, e-mail: ademoor@kub.nl

Jan L.G. Dietz Delft University of Technology, Department ISSE, P.O. Box 356, NL-2600 AI Delft, Email: j.1.g.dietz@its.tudelft.nl

Goran Goldkuhl CMTO, Linkoping university, S-581 83 Linkoping, Sweden; phone +4613281000; fax +4613284435 e-mail ggo@ida.liu.se

Michael S. H. Heng School of Accounting and Information Systems, University of South Australia, Adelaide, Australia, Phone: +61 8 8302 0253, Fax: +61 8 83020992, MichaeI.S.H.Heng@unisa.edu.au

Wolfgang Hesse FB Mathematik und Informatik, UniversiUit Marburg, Hans Meerwein-Str., D-35032 MarburglLahn, Germany, tel: +49-(0)6421-28 21515, hesse@informatik.uni-marburg.de

Anders Hjalmarsson School of Business and Informatics (IDA), University College of Boras, SE 501 90 Boras, Sweden

Suzan Ilcan Department of Sociology and Anthropology, University of Windsor, e-mail: silcan@uwindsor.ca

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Fredrik Karlsson Dept. of Informatics (ESI), Orebro University, SE 701 82 Orebro, Sweden, Phone: +46 19 303000

John Krogstie SINTEF Telecom and Informatics and IDI, NTNU, Forskningsveien 1, N-0314 Oslo, Norway, Phone: +47 22067425, Fax: +47 22067350, Email: J ohn.Krogstie@informatics.sintef.no

Mikael Lind University of Boras, School of Business and Informatics, S-501 90 Boras, Sweden, Phone: +46 33 16 40 00; Fax: +46 33 16 40 07, E-mail: Mikael.Lind@hb.se

Morten Lind 0rsted-DTU: Section of Automation, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark, mli@oersted.dtu.dk

Mark Lycett Department of Information Systems and Computing, BruneI University, Uxbridge, Middlesex, UB8 3PH, Phone: 01895 754000, Fax: 01895 251686, email: mark.lycett@brunel.ac.uk

Robert Macredie Department of Information Systems and Computing, BruneI University, Uxbridge, Middlesex, UB8 3PH, Phone: 01895754000, Fax: 01895 251686, email: robert.macredie@brunel.ac.uk

Valerie A. Martin Department of Information Systems and Computing, BruneI University, Uxbridge, Middlesex, UB8 3PH, Phone: 01895754000, Fax: 01895 251686, email: valerie.martin@brunel.ac. uk

Alexander Mehler Department of Computational Linguistics (LDV), University of Trier, D-54286 Trier, GERMANY, Tel.: +49-651-201-2265 Fax: +49-651-201-3946 Email: mehler@uni-trier.de

Frieder Nake Informatik, UniversiUit Bremen, D-28334 Bremen, Germany, nake@informatik.uni-bremen.de

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Daniel O'Connor Department of Sociology and Anthropology, University of Windsor, Phone:

519-253-4959, Fax: 519-971-3621, e-mail: doconnor@uwindsor.ca

Johannes Petersen Center for Human-Machine Interaction, Oersted * DTU, Automation, Technical University of Denmark, DK-2800 Kongens Lyngby, Phone: +45 45253579, Fax: +4545881295, jop@oersted.dtu.dk

lain W. Phillips Department of Computer Science, Loughborough University, Loughborough, LEll 3TU, UK. Telephone: +44 1509 222680. Fax: +44 1509211586. Email: l.W.Phillips@lboro.ac.uk

Anna Pollard School of Computing, Information Systems & Mathematics, South Bank University, Borough Road, London SEI OAA, Tel: +44 (0) 20 7815 7407, Fax: +44 (0) 2078157499, E-mail: pollara@sbu.ac.uk

J uUana Salles Computer Science Department-ICEX, Federal University of Minas Gerais,

Av. Antonio Carlos 6627, Belo Horizonte - MG - Brazil 31270-010, phone: +5531499-5860, fax: +55 31 499-5858, {jusalles, bigonha}@dcc.ufmg.br

Rob Shields Department of Sociology and Anthropology, Carleton University, e-mail: rshields@ccs.carleton.ca

Edwina Taborsky Bishop's University, Lennoxville, Quebec JIM lZ7, email: taborsky@primus.ca, Tel: Toronto: 416. 361.0898

Delfryn Thomas School of Computing, Information Systems & Mathematics, South Bank University, Borough Road, London SEI OAA, Tel: +44 (0) 207815 7481, Fax: +44 (0) 2078157499, E-mail: del@sbu.ac.uk

Annie Rostlinger CMTO, Linkoping university, S-581 83 Linkoping, Sweden; phone +4613281000; fax +4613284435 e-mail aro@ida.liu.se

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Jim Underwood Department of Information Systems, University of Technology, Sydney, PO Box 123, BROADWAY, 2007, AUSTRALIA, jim@it.uts.edu.au, Phone: +6129514 1831, Fax: +6129514 1807

Hans Weigand Infolab, Tilburg University, P.O.Box Netherlands, tel. +31-13-4663020, weigand@kub.nl

Alex Verrijn-Stuart

90153, 5000 LE Tilburg, The fax +31-13-4663069, e-mail:

University of Leiden, Scheltemakade 15, NL-2012 TD Haarlem, The Netherlands, tel: +31-(0)23-528-1770, alexander.verrynstuart@wxs.nl

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Preface

Organizational Semiotics: Evolving a Science of Information Systems

Ronald Stamper

University of Twente, NL and Staffordshire University, UK, rstamper@compuserve.com

This is the first IFIP Working Conference on Organisational Semiotics. In developing a new subject we need to consider how to proceed. Thus I choose to direct my introductory remarks towards some issues of scientific method of special relevance to our work.

Only a community of scientists can produce a Science. Individuals produce most works of art. Individuals of no particular qualifications decide whether they like or dislike an art work. Scientists must reach consensus about the theoretical ideas and empirical work of their colleagues. This is not a matter of personal taste but to decide about their truth or falsity. Good scientific methods help us to decide these issues.

That contrast between science and art should not suggest that scientists do not also need to be creative. But we should recognise that the constraints imposed by our having to produce a consistent body of knowledge can be quite inhibiting. We are still in an exploratory stage, which calls for creative thinking. It does not matter that an hypothesis is wrong provided that it is subjected to rigorous critical examination. Popper's 'method of bold conjectures and attempted refutations' (ref 1, p. 53) is quite liberating in this respect. Indeed he emphasises that the bolder the theory, the greater its

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content and the greater its risk of refutation but should it survive intense criticism, the greater its value.

Creative thinking has combined with rigorous but considerate criticism characterise this particular scientific community. Although this is the first IFIP meeting on this topic, each of the previous six years has seen an international meeting in this or a closely related area:

1995 1996 1997 1998 1999 2000 2001

Enschede Schloss Dagstuhl Paris Toronto Almelo Stafford Montreal

Organisational Semiotics Computer Semiotics Computer Semiotics Semiotics and fuformation Sciences (ref 2) Organisational Semiotics (ref 3) Organisational Semiotics (ref 4) and now Organisational Semiotics

Of these the last three have published proceedings (Refs 1, 2, 3). These stimulating meetings have played a vital role in building the necessary community and in establishing the commitment to the necessary balance of creativity and criticism.

But before the right scientific conduct can be established we must build the scientific community. The face-to-face meetings have played an essential role in that. Of course an established science will at least appear to be conducted in its literature. A new and rapidly evolving discipline has greater need for a constant interchange of views and, more especially of talk. We are still developing a stable vocabulary that will allow us to use our shared concepts without risk of confusion. The editing process has contributed to that exchange by arranging more than the usual amount of commentary to help the authors make their revisions. We still have more community building, more work to do on forming a stable terminology and some basic concepts still call for discussion.

These papers provide evidence of convergence within our community, especially on the problems we must address and on many of the key concepts. Let us examine some of them, with special regard for issues of scientific method, while adding to our research agenda.

Foremost is our agreement to build a science of information systems using the operationally secure, primitive notion of a sign. On that concept others can be constructed with sound operational definitions. The conceptual strength of signs is that we can demonstrate what signs do and how people use them for getting things done in organisations. This operational clarity allows signs to be studied empirically, an essential for a science of information. The Peircean semiotic triangle (a sign-token representing some thing through an interpret ant) suits most authors - especially as Peirce's 'interpretant' at the apex allows some ambiguity of interpretation. It also

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induces more elaborate models involving two or more linked triangles, a notion I shall employ below.

Departing from the mainstream of IFIP thinking, we largely accept that reality (as far as we can know it) is a social construction that depends on our use of signs. Some who take it that our social reality is socially constructed may balk at arguing that the physical reality we know is also socially created. Personally I go all the way, because I feel great discomfort adopting an objectivist position with respect to some of reality and a social constructivist for the rest - but I'm ready to justify my views any time! This key issue should remain high on our agenda because it underpins so many other aspects of our work.

We can see social construction in action within these conference papers, which examine many basic concepts. A science of information systems must stabilise its main concepts. So, methodologically we must ask how should do that.

In this meeting several authors contributed to the construction of the concepts:

data information knowledge No one will deny the central roles that these play in both theory and

practice. Most of our colleagues tend to treat these notions as basic ones that are self-explanatory. Recognising the danger of this naIve assumption, our sponsoring Working Group, IFIP WG 8.1, created the FRISCO Task Group in 1988 to arrive at a "framework of information systems concepts", including data, information and knowledge and many others (Falkenberg et al. 1998). Over a period of ten years, meeting roughly each quarter for two or three days, writing papers and disputing about them, we failed to reach unanimity: hence the continuing importance, relevance and difficulty of these issues.

Semiotic applied to Organisational Semiotics provides some methodological guidance on this problem. Definition plays a key part in our discussions. To help us reach some agreement about what constitutes a good definition, we can use the Semiotic Triangle about which we already agree with Peirce: "A sign . . . is something which stands to somebody for something in some respect or capacity." As Peirce states, this creates another sign, which he calls "the interpretant of the first sign." A definition can play the part of an interpretant. A definition (a sign, of course) expresses the link between the term we wish to define, the definiendum, and the expression that explains it, the definiens. A good definition will substitute for the definiendum terms that are more concrete and, ideally, easier to understand. As a secure basis for theory construction and empirical studies, we need to link our terms firmly to 'reality' (whatever we mean by that difficult term)

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so that our colleagues will all make the same connection between sign and object. So what criteria must a good definition satisfy?

Definition is not just a matter of words. For a definition to work, the people using it must share a number of skills that are revealed in Figure 1, an expanded version of the Semiotic Triangle.

men. me . ign-vehicle. an operationally demooslJ'3bly stable pallem

norm for the perception of sign vehicle of the deliniendum

DEFlNmON a nonn relating defmiendum to a structure of ob jetts In me dcfulicns

relationship imputed by the definition

Dorms for the perception of objects used in the definiendum

object referred to. a complex stable consbtlct that is operationally demonstrable

Figure 1. Definition as a norm linking sign and object as the interpretant in a Peircean Triangle

The figure shows the definition at the apex as a norm, that is to say a pattern of behaviour that users of the definition must shares. The interpretant is a complex norm and the semiotic triangle shows how we build it from other norms. Henceforth I shall use the term definition to refer to this norm.

Before we can construct any definition that a community can share, the members must share certain perceptions about the world. We must recognIse: a) the sign-vehicles used in the definition in any of their accepted shapes or

forms; and b) the objects that the terms in the definiens stand for.

We want the users of the definition to link the definiendum to reality in an operationally reliable manner. Usually the definiens is a complex verbal expression incorporating many other terms that people mayor may not be expected to know. If they do not know the terms we are using in the definiens, we can recursively define them and any terms they employ using other verbal expressions. The challenge is to escape from this chain of verbal

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definitions that only trade words for words. This is where the strategy of examining the norms involved can help us.

The figure shows that the definition is a norm built upon two other norms, both perceptual norms. The users of the definition must have acquired these perceptual skills. We should be able to check operationally in our discussions that other members of the community who want to use the definition actually do share these perceptual norms and can use them in a reliable way.

This is trivially easy for a) the sign-vehicles. We can produce any number of concrete examples of them. The sign-vehicle must necessarily be a sign-type so that we can use different sign-tokens of that type in any number of places at any number of times when printed in various fonts (dog, DOG, dog, dog ... ) or uttered various accents. This is easy for a literate community, they have the requisite linguistic norms.

The difficulty arises with the perception of the complex of objects used in the definiens. In the simplest case, the readers can relate all the terms in the definiens to real objects directly, that is without having recourse to further definitions. If we can deal with this simplest case, we can deal also with chains of definitions by applying our solution recursively. Eventually we reach the simple case. At that point the interpretant ceases to be a purely linguistic norm. Unless it links a linguistic type to an object or type of object, fails to bridge the gap between the semiological realm and reality. Reality is the world we experience directly, the world where scientists conduct experiments and about which they find inter-subjective agreement. The difficulty arises when different parts of the community include different categories of things in the reality about which they theorise when they reach the primitive notions that recursively underpin their all their other definitions.

These privileged terms that we can understand without the aid of verbal definitions are a most precious resource: they serve as the foundation for the definitions of other concepts. To find them, we must look for terms that stand for things that we can learn to recognise, intersubjecively and operationally by a mixture of doing and talking. This we term 'ostensive defmition' to distinguish it from the purely linguistic kind of definition.

The importance of the concept of a sign for an empirical science lies in the fact that we can define it ostensively. Thus we can introduce anyone to the verbal expression 'a sign' and then proceed to show them any number of diverse, concrete examples, while probably giving them a commentary on how some people use the sign to stand for something or other in some circumstances. For example, find a board with the words "Low bridge - 3m clearance" at a road junction; this physical object has physical properties - it might fall on your head, in which case it would help demonstrate some

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aspect of physics - but its function as a sign is revealed by watching the high lorries that take the other route because the driver does not have physically to encounter the bridge to know how best to act. After every few such illustrative demonstrations of our use of one thing (the sign) to stand for another (the object), we ask our student to supply an example of her own so that we can check that she is using the term 'sign' in the accepted way. In this way we teach the student the perceptual norm that does not involve language. The sign 'sign' understood this way is not an object in the mind of any person but it belongs in a world of real actions that we can demonstrate to anyone. At least we start, in this Working Conference, with this, one, well-founded, primitive notion.

Where do we go from there? The papers we shall be considering offer a variety of routes for us to consider. To add to the material for discussion, and to illustrate my points about definition, let me outline my own treatment of the term 'information'.

My own journey into the field of organisational semiotics began when I tried to be precise when using the word 'information'. It has so many meanings that I was led to use sign as a primitive notion. To introduce greater precision into the everyday, intuitive understanding of 'information' we can begin my examining some of its uses. For example: 1. "The CIA can give us massive amounts of information - gigabyte after

gigabyte!" 2. "We get almost as much information about a person's lung capacity from

their age, height and weight as measuring it directly with this complicated machine."

3. "Given what we know about the disposition of the enemy's forces, from this intercepted message sent by their general we can deduce all the information we need for our defence."

4. "As a manager, I get a surprising amount of information about the morale of the staff by taking lunch with them whenever possible."

5. "All that speculation about the case contains far less information than this short statement from the lawyer on the other side."

6. "This manual contains all the information you need on routine repairs to this engine." In the various cases the speaker uses the sign 'information' in a different

"respect or capacity", and hence with quite different meanings. When thinking about definitions, we have a tendency to ask "What is the

meaning of 'information' (or anything else)?" as though there must be one answer only, given that we have only one word. We are even tempted to reify information, treating it as a kind of mysterious substance that 'is contained in' (as we say) the sentences we use.

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To overcome that misconception, notice in the above examples of everyday usage a number of quite different "respects or capacities", relating to our talk of 'information'. In the six cases the speakers have the following very different concerns: 1. The physical load of information - perhaps they are discussing how to

store all the information they will be given. They are not concerned what it means to anyone or how it will be used. So we can measure their information as a number of distinct patterns, the bytes they can count. (ref 6. Fig 10, p. 387)

2. The empiric properties of one stream of observations that may be found in the medical records (age, height and weight) that can save time making costly observations. So their best measure is mutual information, a precise statistical parameter derived from a large set of repetitive data, and based on empirical probability. (ref 6. Fig 11, p. 388)

3. The syntactic properties relating to a unique situation which has been mathematically modelled so that every item of intelligence can limit the range of possible scenarios. So their measure of information might be (though I have never seen this applied in practice, yet) one of the Carnap­Bar Hillel measures based on logical probability. (ref 6, Fig. 12. p. 390)

4. The semantic properties of observations that rely almost entirely on intuition. Experience enables the manager to know what they mean and he can take them into account in his decision-making, we may never know how although we might build a syntactic model (as in 3) to theorise about it. However, in respect of a given decision about a threatened strike, and in the manager's capacity as a negotiator, we can measure how he adjusts his odds. So we can measure the information he derives from the last lunchtime encounter using the subjective probabilities he assigns to a number of possible outcomes. (ref 6, Fig 13. p. 392)

5. The pragmatic properties of the communications in the legal dispute they are discussing concern the obligations, hopes, expectations etc that the messages generate, the odds on outcomes (as in 4) may be taken into account here. So we need but do not yet have a satisfactory measure of pragmatic information, although the Language Action Paradigm could probably provide a solution by measuring the information with respect to a limited model of a conversation. (ref 6, Fig. 14, p. 394)

6. The social product of assembling data from many observations and theories consists of rules, advice, engineering and scientific laws as well as some ordinary laws relating to the safety of the engine. 'Information' is being used here as a synonym for 'knowledge'. To measure information in this sense, we should have to find canonical representation for items of knowledge in order to begin constructing a measurement. As a first step we shall need an operationally precise definition of

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'knowledge' and my own preference is to base it on the equality knowledge = norms. (ref 6, Fig. 15, p 395) So we have six different meanings of 'information' precise enough to

lead to many operationally well-defined measurements, certainly in cases 1 to 4, and with a little more effort for cases 5 and 6. Each of these definitions captures some but never all aspects of the common sense, intuitive notion of information that now confuses talk of information systems because of its seductively dangerous, slippery mixture of meanings. Information looks like a straightforward concept but is not.

Working on organisational semiotics, we are duty bound to find ways of anchoring our key concepts to 'reality' by operational means that anyone can test in a public arena. You may think that this is unnecessary or over ambitious. I have certainly often encountered the view that ordinary usage is good enough, and I have even been told at meetings of another IFIP working group that being very precise is dangerous because it hinders imaginative thinking.

I am all in favour of imaginative thinking. We must often introduce terms that capture the flavour of our thoughts well before we can pin upon them exact meanings. In the early development of a new subject such as organisational semiotics, I would expect this to be quite common. Nevertheless, every such imaginative usage should automatically raise the question: How do we make the meaning of this term clear and operational? My own attempt to do this I have given in ref. ?? and urge my colleagues to examine it critically. The best way of doing that is by using the more precise definitions to find whether or not they work in practice.

The underlying six levels that I introduced as SF, a Semiotic Framework (or Ladder), have been applied by several authors in these proceedings. It is gratifying to see that this simple analytical tool used to elucidate so many different issues. Finally I feel it appropriate to provide a note on this Framework to help colleagues who might be interested in using it.

The Framework is based on a few simple principles: - Anything can function as a sign, possibly in many ways simultaneously. - Certain sets of properties of signs can be studied independently of other

sets of properties. These sets of properties define the levels on the SF. - On the different levels of the SF we have different basic units to

investigate. - You can take someone by the hand to see these units and become

acquainted with performing operations on them, counting them, testing their relationships etc. (ostensive definition).

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LEVEL UNITS INVESTIGATED

Level Units investigated physical sign-tokens

(physical objects and events) categories of tokens (eg: spoken ones)

empiric streams of sign-tokens apparatus capable of displaying varieties of states (channels, storage)

syntactic forms of sign-types well-formed formulas formal transformations

semantic sign-types in sign roles perceivable things the labels we give them

pragmatic acts performed by agents using unique sign-tokens to influence other agents signs incorporating expressions of intention

social attitudes and norms held by individuals sign events that change the attitudes and norms

PHENOMENA OBSERVED

Phenomena observed their physical properties eg: weight, duration, position

their statistical properties their combinatorial properties

their structures and the permitted manipulations of them

their [meaning] relationships denotation and signification

how and by whom these are related (eg: ontological dependency)

success or failure of the sign-acts relations among agents and other

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conditions making sign-acts effective relationships among the acts

regularities of behaviour of the individuals rules that signify norms

One can also narrow down investigations by looking at the X level aspects of a Y level issue eg: the semantic aspects of illocutions (a pragmatic issue).

- The cells of an SF by SF matrix can be used to narrow down discussions of sign properties even more closely. Let me propose that we could usefully clarify our terminology by

examining the meanings of our key terms on each level or even within each cell of the SF by SF matrix. This should help us to remove the mystery and semantic confusion surrounding 'information', 'knowledge', 'meaning', 'communication', 'relevance' and other essential but challenging words. Without their having precise, operational meanings, we shall be unable to reach agreement over any theories about them. Moreover, those theories will evade and attempts at empirical testing by the simple device of adjusting the meaning to fit our observations.

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REFERENCES

1. Popper, Sir Karl, 1979, Objective Knowledge: An Evolutionary Approach, Oxford, Oxford University Press.

2. Paerron, Paul, Marcel Danei, Jean Umiker-Sebeok and Anthony Watanebe, 2000, Semiotics and Information Sciences, Toronto, LEGAS.

3. Liu, Kecheng, Rodney 1. Clarke, Peter B0gh Andersen and Ronald KStamper (eds), 2001, Information, Organisation and Technology: Studies in Organisational Semiotics, Boston, Dordrecht, London, Kluwer Academic.

4. Liu, Kecheng, Rodney 1. Clarke, Peter B0gh Andersen and Ronald KStamper (eds), 2001, Coordination and Communication Using Signs: Studies in Organisational Semiotics - 2, Boston, Dordrecht, London, Kluwer Academic,. ISBN 0-7923-7509-2

5. Falkenberg, E, W. Hesse, P. Lindgreen, B.E. Nilsson, 1.L.H. Oei, C. Rolland, R.K Stamper, F. 1.M. van Assche, A.A. Verrijn-Stuart, K. Voss, 1998. A Framework of Information Systems Concepts, IFIP Geneva, on line edition at ftp://ftp.leidenuniv.nllfri-full.zip

6. Stamper, RK, 1996, "Signs, Infonnation, Norms and Systems", in Berit Holmqvist, Peter B. Andersen, Heinz Klein and Roland Posner (eds), Signs at Work, De Gruyter, Berlin, ISBN: 90-14-05425-2, pp.349-397.

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